Cardiovascular diseases (CVDs) in India and globally are the major cause of mortality, as revealed by the World Health Organization (WHO). The irregularities in the pace of heartbeats, called cardiac arrhythmias or heart arrhythmias, are one of the commonly diagnosed CVDs caused by ischemic heart disease, hypertension, alcohol intake, and stressful lifestyle. Other than the listed CVDs, the abnormality in the cardiac rhythm caused by the long term mental stress (stimulated by Autonomic Nervous System (ANS)) is a challenging issue for researchers. Early detection of cardiac arrhythmias through automatic electronic techniques is an important research field since the invention of electrocardiogram (ECG or EKG) and advanced machine learning algorithms. ECG (EKG) provides the record of variations in electrical activity associated with the cardiac cycle, used by cardiologists and researchers as a gold standard to study the heart function. The present work is aimed to provide an extensive survey of work done by researchers in the area of automated ECG analysis and classification of regular & irregular classes of heartbeats by conventional and modern artificial intelligence (AI) methods. The artificial intelligence (AI) based methods have emerged popularly during the last decade for the automatic and early diagnosis of clinical symptoms of arrhythmias. In this work, the literature is explored for the last two decades to review the performance of AI and other computer-based techniques to analyze the ECG signals for the prediction of cardiac (heart rhythm) disorders. The existing ECG feature extraction techniques and machine learning (ML) methods used for ECG signal analysis and classification are compared using the performance metrics like specificity, sensitivity, accuracy, positive predictivity value, etc. Some popular AI methods, which include, artificial neural networks (ANN), Fuzzy logic systems, and other machine learning algorithms (support vector machines (SVM), k-nearest neighbor (KNN), etc.) are considered in this review work for the applications of cardiac arrhythmia classification. The popular ECG databases available publicly to evaluate the classification accuracy of the classifier are also mentioned. The aim is to provide the reader, the prerequisites, the methods used in the last two decades, and the systematic approach, all at one place to further purse a research work in the area of cardiovascular abnormalities detection using the ECG signal. As a contribution to the current work, future challenges for real-time remote ECG acquisition and analysis using the emerging technologies like wireless body sensor network (WBSN) and the internet of things (IoT) are identified.

Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges

An emerging research direction considers the inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) to bring about the synergy between the easy measurability of PPG and the rich clinical knowledge of ECG to facilitate preventive healthcare. Previous reconstruction using a universal basis has limited accuracy due to the lack of rich representative power. This paper proposes a cross-domain joint dictionary learning (XDJDL) framework to maximize the expressive power for the two cross-domain signals. Building on K-SVD technique, XDJDL optimizes simultaneously the PPG and ECG signal representations and the transform between them, enabling the joint learning of a pair of signal dictionaries with a transform to characterize the relation between their sparse codes. The proposed model is evaluated with 34,000+ ECG/PPG cycle pairs containing a variety of ECG morphologies and cardiovascular diseases. Experimental results validate the accuracy and the generality of the proposed algorithm, suggesting an encouraging potential for disease screening using PPG measurement based on the proactive learned PPG-ECG relationship.

Cross-Domain Joint Dictionary Learning for ECG Reconstruction from PPG

An Electrocardiogram (ECG) is defined as a test that is performed on the heart to detect any abnormalities in the cardiac cycle. Automatic classification of ECG has evolved as an emerging tool in medical diagnosis for effective treatments. The work proposed in this paper has been implemented using MATLAB. In this paper, we have proposed an efficient method to classify the ECG into normal and abnormal as well as classify the various abnormalities. To brief it, after the collection and filtering the ECG signal, morphological and dynamic features from the signal were obtained which was followed by two step classification method based on the traits and characteristic evaluation. ECG signals in this work are collected from MIT-BIH, AHA, ESC, UCI databases. In addition to this, this paper also provides a comparative study of various methods proposed via different techniques. The proposed technique used helped us process, analyze and classify the ECG signals with an accuracy of 97% and with good convenience.

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Classification of Arrhythmia from ECG Signals using MATLAB

The object of the proposed paper is to design and analyze the performance of a non-contact heart rate variability (HRV) measuring device based on ultrasound transducers. The rationale behind non-contact HRV measurement is the goal of obtaining a means of long term monitoring of a patient’s heart performance. Due to its complexity as a non-contact measuring device, influential physical quantities, error source and other perturbations were thoroughly investigated. For medical purposes it is of utmost importance to define the target uncertainty of a measuring method from the side of physicians, while it is the role of scientists to realistically evaluate all uncertainty contributions. Within this paper we present a novelty method of non-contact HRV measurement based on ultrasound transducers operating at two frequencies simultaneously. We report laboratory results and clinical evaluations are given for healthy subjects as well as patients with known heart conditions. Furthermore, laboratory tests were conducted on subjects during a relaxation period, and after 1 min physical activity

Design and Clinical Evaluation of a Non-Contact Heart Rate Variability Measuring Device.

Arrhythmia and heart problems are one of the most important health problems in the whole world which leads to various other severe complications, for example, heart attack. As arrhythmia is a type of cardiologic disease, it can be used for pointing out the abnormality from normal heart activity and try to understand about heartbeat whether the heartbeat is normal or not. The main element that only a less number of people informed being discovered as a result of screening indicates that there are missing opportunities to prevent heart disease. There are different methods present for heart. Heart diseases are recognized by capturing information from patient’s body and forward results to doctors to reduce the risk of heart attack. So, the researcher always keeps trying to find out the best solution for this problem. The researchers have done huge research on this area, so according to the comparison between various techniques which are used for classification of arrhythmia, they prefer to use machine learning algorithm to achieve high performance and better accuracy.

Classification of Arrhythmia Using Machine Learning Techniques

ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. This feature extraction scheme determines the amplitudes and interval in the ECG signal for subsequent analysis. The amplitude and interval of P-QRS-T segment determine the function of heart. Cardiac Arrhythmia shows a condition of abnormal electrical activity in the heart which is a threat to humans. The aim of this paper presents analyses cardiac disease in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia using analysis of resulting ECG normal & abnormal wave forms. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the features, for the classification of heart beats according to different arrhythmia. Cardiac arrhythmia which are found are Tachycardia, Bradycardia, Supra ventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia, hence abnormalities of heart may cause sudden cardiac arrest or cause damage of heart. The early detection of arrhythmia is very important for the cardiac patients. Electrocardiogram (ECG) feature extraction system has been developed and evaluated based on the multi-resolution wavelet transform.

/pdf/a-review-paper-on-analysis-ofelectrocardiograph-ecg-signal-4me8i9bfei.pdf

A Review Paper on Analysis ofElectrocardiograph (ECG) Signal for theDetection of Arrhythmia Abnormalities

3 ABSTRACT - ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. This feature extraction scheme determines the amplitudes and interval in the ECG signal for subsequent analysis. The amplitude and interval of P-QRS-T segment determine the function of heart. Cardiac Arrhythmia shows a condition of abnormal electrical activity in the heart which is a threat to humans. The aim of this paper presents analyses cardiac disease in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia using analysis of resulting ECG normal & abnormal wave forms. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the features, for the classification of heart beats according to different arrhythmia. Cardiac arrhythmia which are found are Tachycardia, Bradycardia, Supra ventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia, hence abnormalities of heart may cause sudden cardiac arrest or cause damage of heart. The early detection of arrhythmia is very important for the cardiac patients. Electrocardiogram (ECG) feature extraction system has been developed and evaluated based on the multi-resolution wavelet transform.

/pdf/a-review-paper-on-analysis-of-electrocardiograph-ecg-signal-4ixbvt5pgo.pdf

Arun Tomar

Papers

A Review Paper on Analysis ofElectrocardiograph (ECG) Signal for theDetection of Arrhythmia Abnormalities

A Review Paper on Analysis of Electrocardiograph (ECG) Signal for the Detection of Arrhythmia Abnormalities